How would you work with engineering to reduce latency in Radar

Problem Statement Description

Product context: Stripe is financial infrastructure for internet businesses; its products include payments, Checkout, Billing, Connect, Radar, Issuing, Terminal, and tax tools.

Stripe Radar evaluates payment risk in the critical path of online transactions, where even small increases in decision latency can affect authorization completion, checkout conversion, merchant trust, and the end-customer experience. At the same time, Radar must preserve fraud-detection quality, reliability, compliance, and merchant configurability across diverse payment methods, geographies, traffic patterns, and risk profiles.

In this Technical PM interview, describe how you would partner with engineering to reduce Radar latency. Focus on how you would frame the problem, align on technical and product requirements, identify where latency is introduced, and make trade-offs between speed, accuracy, reliability, and merchant control. Assume you are working with risk engineering, payments infrastructure, data science, platform reliability, and customer-facing teams.

Your answer should show how you think about an API-driven, real-time financial infrastructure product where latency improvements must be measurable, safe to roll out, and useful to merchants without weakening fraud protections or introducing operational risk.

The experience should consider:

- The end-to-end Radar decision path, including payment request intake, feature retrieval, model/rule evaluation, decisioning, and response to the payment flow.

- Clear latency metrics such as p50, p95, p99, timeout rates, and merchant- or region-specific cohorts, along with how they relate to payment success and fraud outcomes.

- Instrumentation needed to isolate bottlenecks across services, data dependencies, network calls, model inference, rules execution, and downstream payments systems.

- Product and technical requirements for acceptable latency, reliability, fallback behavior, accuracy, auditability, and merchant-facing transparency.

- Trade-offs between reducing latency and maintaining fraud detection quality, compliance obligations, explainability, and custom rule flexibility.

- Rollout considerations such as experiments, staged deployment, merchant segmentation, monitoring, alerting, incident response, and rollback criteria.

- Cross-functional collaboration with engineering, data science, risk operations, support, and merchant-facing teams to prioritize work and communicate impact.

The goal is to demonstrate how you would lead a technically grounded latency-reduction effort for Stripe Radar: defining the right problem, aligning teams on constraints, using data to guide prioritization, managing risk during rollout, and ensuring the resulting improvements support merchant growth, financial reliability, and a strong developer experience.